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sentiment-priority-scorer情绪优先评分者

Agent Skill

sentiment-priority-scorer 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

19,339

周安装

790

GitHub Stars

公开资料未说明

下载量

6,257
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:sentiment-priority-scorer(情绪优先评分者)
来源仓库:https://github.com/vishalgojha/sentiment-priority-scorer
安装命令:
openclaw skills install sentiment-priority-scorer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install sentiment-priority-scorer

简介

情绪优先评分者用于标准化房地产线索的优先级排序,基于情绪、意图等多维因子。

  • 适合在 OpenClaw 中提升销售线索处理效率的任务场景。
  • 通过 clawhub 安装并使用 openclaw skills install sentiment-priority-scorer 命令部署。
  • 应核实评分逻辑是否符合实际业务规则,避免依赖单一信号做决策。
  • 适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
sentiment-priority-scorer
description
Score normalized real-estate leads using sentiment, urgency, intent, recency, and record type to produce deterministic priority rankings and P1-P3 buckets. Use when users ask to prioritize hot leads, rank callback queue, or triage follow-ups without performing writes or outbound sends. Recommended chain: india-location-normalizer then sentiment-priority-scorer then summary-generator and action-suggester.

Sentiment Priority Scorer

Produce deterministic priority scores for leads without mutating any state.

Quick Triggers

  • Rank leads by urgency and tone for callback priority.
  • Classify leads into P1/P2/P3 queue.
  • Score follow-up priority from normalized lead records.

Recommended Chain

india-location-normalizer -> sentiment-priority-scorer -> summary-generator

Execute Workflow

  1. Accept input from Supervisor containing normalized leads.
  2. Validate input with references/sentiment-priority-input.schema.json.
  3. Score each lead with:

- sentiment_score in range [-1, 1] - intent_score in range [0, 1] - recency_score in range [0, 1] - mapped urgency_score from lead urgency (high=1.0, medium=0.6, low=0.3)

  1. Use record_type to avoid over-prioritizing generic bulk inventory:

- buyer_requirement: apply +0.10 intent lift (hard demand signal) - inventory_listing: no lift unless high-action cues are present

  1. Boost intent_score when high-action cues exist in listing text:

- immediately, keys at office, one day notice, possession, inspection any time

  1. Compute priority_score on a 0-100 scale:

- priority_score = 100 * (0.40*urgency_score + 0.30*intent_score + 0.20*recency_score + 0.10*sentiment_risk) - sentiment_risk = max(0, -sentiment_score)

  1. Assign buckets:

- P1 for priority_score >= 75 - P2 for priority_score >= 50 and < 75 - P3 for < 50

  1. Produce plain-language evidence tokens that explain the score, including record-type evidence.
  2. Validate output with references/sentiment-priority-output.schema.json.

Enforce Boundaries

  • Never write to Google Sheets, databases, or files.
  • Never send messages or trigger outbound channels.
  • Never create reminders or execute actions.
  • Never bypass Supervisor routing or approvals.
  • Never replace upstream urgency; only derive scoring fields.

Handle Errors

  1. Reject schema-invalid inputs.
  2. Return field-level reasons when scoring cannot be computed.
  3. Fail closed if required scoring features are missing.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

90.91%
按下载量换算5,688

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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